Systems and methods for intelligently optimizing a queue of actions in an interface using machine learning

ABSTRACT

Systems and methods for optimizing processing of loan applications are disclosed. A system may include one or more memory devices storing instructions and one or more processors configured to execute the instructions. The instructions may instruct the system to analyze training data to build a predictive model. The instructions may also instruct the system to apply the predictive model to a loan application to determine a first probability of an institution funding the loan application if the institution proactively engages a loan-arranging entity of the loan application and a second probability of the institution funding the loan application if the institution reactively engages the loan-arranging entity. The instructions may further instruct the system to reporting the first and second probabilities to a user through a user interface.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/660,751 filed Oct. 22, 2019, the complete disclosure of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to data processing systems, andmore particularly, to systems and methods for intelligently optimizing aqueue of actions in an interface using machine learning.

BACKGROUND

Consumers often seek financing from institutions to finance certaintypes of purchases, including, for example, automobile purchases.Automobile dealerships may act as loan-arranging entities who helparrange the financing, and they typically send loan applications tomultiple financial institutions (e.g., prospective lenders), who maycompetitively bid to fund the loan applications. Relationship managersor loan officers working for the institutions may evaluate the loanapplications and seek to originate the loans on terms that areacceptable to the institutions, the dealers, and the consumers.

One of the jobs of a relationship manager may be to contact thedealerships regarding the loan applications assigned to the relationshipmanager. Contacting the dealerships may help improve the likelihood thatthe loan applications will be funded through the institutions for whomthe relationship manager works. To that end, the relationship managermay utilize a tool capable of providing an interface allowing therelationship manager to view and track the loan applications assigned tothe relationship manager. The relationship manager may also utilize theinterface to work on the loans back and forth with the dealerships.

Existing tools, however, have deficiencies including the inability tointelligently prioritize actions for the relationship manager. Due tothe high volume of loan applications pending review by the relationshipmanager, the relationship manager may not be able to evaluate everyapplication manually. The relationship manager may therefore be forcedto rely on his or her experience to prioritize and decide which loanapplications to engage. Such decisions are often suboptimal.

Accordingly, a need exists for improved systems and methods forintelligently optimizing a queue of actions in an interface.

BRIEF SUMMARY

The disclosed embodiments include systems and methods for intelligentlyoptimizing a queue of actions in an interface. In some embodiments, thequeue of actions may relate to intelligent processing of loanapplications.

In one embodiment, a system is disclosed. The system may include one ormore memory devices storing instructions and one or more processorsconfigured to execute the instructions. The instructions may instructthe system to analyze training data to build a predictive model. Theinstructions may also instruct the system to apply the predictive modelto a loan application to determine a first probability of an institutionfunding the loan application if the institution proactively engages aloan-arranging entity of the loan application and a second probabilityof the institution funding the loan application if the institutionreactively engages the loan-arranging entity. The instructions mayfurther instruct the system to reporting the first and secondprobabilities to a user through a user interface.

In another embodiment, a method is disclosed. The method may includeanalyzing training data to build a predictive model. The method may alsoinclude applying the predictive model to a plurality of loanapplications to determine a first probability of an institution fundingthe loan applications if the institution proactively engages loanarranging entities of the loan applications and a second probability ofthe institution funding the loan applications if the institutionreactively engages the loan arranging entities of the loan applications.The method may further include ranking the loan applications based onthe first and second probabilities and reporting the loan applicationsin a ranking order to a user through a user interface.

In another embodiment, a non-transitory memory is disclosed. Thenon-transitory memory may store instructions that, when executed by atleast one processor, cause a system to perform operations. Theoperations may include analyzing training data to build a predictivemodel. The operations may also include applying the predictive model toa plurality of loan applications, to determine a first probability of aninstitution funding the loan applications if the institution proactivelyengages loan-arranging entities of the loan applications and a secondprobability of the institution funding the loan applications if theinstitution reactively engages the loan-arranging entities of the loanapplications. The operations may further include ranking the loanapplications based on the first and second probabilities and reportingthe loan applications in a ranking order to a user through a userinterface.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate exemplary disclosed embodimentsand, together with the description, serve to explain the disclosedembodiments. In the drawings:

FIG. 1 is a block diagram illustrating an exemplary loan applicationqueuing system, consistent with disclosed embodiments.

FIG. 2 is an exemplary cloud-computing environment for optimizingprocessing of loan applications using machine learning, consistent withdisclosed embodiments.

FIG. 3 is a flow diagram of an exemplary method for optimizingprocessing of loan applications, consistent with disclosed embodiments.

FIG. 4 is an illustration depicting an exemplary user interface,consistent with disclosed embodiments.

DETAILED DESCRIPTION

Reference will now be made to exemplary embodiments, examples of whichare illustrated in the accompanying drawings and disclosed herein.Wherever convenient, the same reference numbers will be used throughoutthe drawings to refer to the same or like parts.

FIG. 1 is a block diagram illustrating an exemplary intelligentinterface queuing system 100, consistent with disclosed embodiments.System 100 may include one or more dedicated processors 102, such asapplication-specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), or various other types of processors, coupled withone or more non-transitory processor-readable memory devices 104configured for storing processor-executable code. The system 100 mayalso include one or more interfaces, such as a data interface 106, auser interface 108, and an application interface 110, to interact withdatabases 112, users, and other computing systems.

In some embodiments, one or more processors 102 may operate as a part ofa cloud-computing system. FIG. 2 depicts an exemplary cloud-computingsystem 200. System 200 can be configured to expose an interface forcommunication with other systems, including system 100 (FIG. 1). System200 can include computing resources 202, which may include one or moreprocessors 102. System 200 may also include other resources such asdatabases 204, communication interface 206, and the like. Thesecomponents of system 200 can be configured to communicate with eachother, or with external components of system 200, using a network 208.The particular arrangement of components depicted in FIG. 2 is notintended to be limiting. System 200 can include additional components,or fewer components. Multiple components of system 200 can beimplemented using the same physical computing device or differentphysical computing devices.

Computing resources 202 can include one or more computing devicesconfigurable to train data models. The computing devices can bespecial-purpose computing devices, such as graphical processing units(GPUs) or application-specific integrated circuits, or general-purposecomputing devices. The computing devices can be configured to host anenvironment for training data models. For example, the computing devicescan host virtual machines, pods, or containers. The computing devicescan be configured to run applications for generating data models. Forexample, the computing devices can be configured to run Amazon WebServices (AWS) SageMaker, Tensorflow, or similar machine-learningtraining applications. Computing resources 202 can be configured toreceive models for training from model optimizers, model storagedevices, or other components of system 200. Computing resources 202 canbe configured to provide training results, including trained models andmodel information, such as the type and/or purpose of the model and anymeasures of classification error.

Database 204 can include one or more databases configured to store datafor use by system 200. For example, database 204 may store training datafor machine learning or neural network training. The databases caninclude cloud-based databases (e.g., AMAZON WEB SERVICES S3 buckets) oron-premises databases.

Interface 206 can be configured to manage interactions between system200 and other systems using network 208. In some aspects, interface 206can be configured to publish data received from other components ofsystem 200 (e.g., computing resources 202, database 204, or the like).This data can be published in a publication and subscription framework(e.g., using APACHE KAFKA), through a network socket, in response toqueries from other systems, or using other known methods. In variousaspects, interface 206 can be configured to provide data or instructionsreceived from other systems to components of system 200. For example,interface 206 can be configured to receive instructions for generatingdata models (e.g., type of data model, data model parameters, trainingdata indicators, training parameters, or the like) from another systemand provide this information to computing resources 202. As anadditional example, interface 206 can be configured to receive dataincluding sensitive portions from another system (e.g. in a file, amessage in a publication and subscription framework, a network socket,or the like) and provide that data to database 204.

Network 208 can include any combination of electronics communicationsnetworks enabling communication between components of system 200. Forexample, network 208 may include the Internet and/or any type of widearea network, an intranet, a metropolitan area network, a local areanetwork (LAN), a wireless network, a cellular communications network, aBluetooth network, a radio network, a device bus, or any other type ofelectronics communications network know to one of skill in the art. Itis to be understood that system 200 is depicted merely as an example forimplementing a cloud-computing system. It is also to be understood thatfor illustrative purposes, the description below may reference processor102 and system 100 as the system configured to carry out the variousoperations. However, the references to processor 102 and system 100 aremerely provided as examples and are not meant to be limiting. It iscontemplated that computing resources 202 and system 200 may beconfigured to carry out the various operations disclosed herein withoutdeparting from the spirit and scope of the present disclosure.

When instructions are executed by processor 102, processor 102 mayperform operations including analyzing a set of training data to build apredictive model 114 using machine learning. Processor 102 may alsoapply predictive model 114 to a queue of actions to intelligentlyprioritize the actions. For example, processor 102 may utilizepredictive model 114 to analyze historical data to identify a likelihoodfor completing certain tasks or actions between a first party and asecond party. Processor 102 may also utilize predictive model 114 toconsider or learn a number of factors relevant to the completion of thetasks or actions. For instance, in some embodiments, predictive model114 may take into account the interactions between the first party andthe second party to determine how to prioritize the tasks. In someembodiments, predictive model 114 may also take into account whether thefirst party proactively or reactively engaged with the second party todetermine how to prioritize the tasks.

In some embodiments, the first party may include a relationship managerworking for a financial institution, the second party may include one ormore entities arranging the loan applications (e.g., a dealerships), andthe tasks may include one or more loan applications pending review bythe relationship manager. Processor 102 may apply predictive model 114to these loan applications to determine the values of these loanapplications to the institution and prioritize these loan applicationsaccordingly. Processor 102 may therefore provide the abilities toanalyze historical data and intelligently prioritize future tasks, bothof which cannot not be done by the relationship manager manually ormentally due to the high volume of loan applications and because most ofthe historical data is recorded in manners not readily understandable bythe relationship manager. Furthermore, the relationship manager does nothave the mental capacity to keep track of all interactions between therelationship manager and the various dealerships, let alone analyzingthese interactions to identify factors relevant to the completion of theloan applications. Processor 102 can thus be utilized to improve the waythe relationship managers manage and prioritize the loan applicationspending their review.

In some embodiments, values of the loan applications may be determinedby taking into consideration the probabilities of the institutionfunding the loan applications. In some embodiments, the predictive modelmay predict the probabilities of the institution funding the loanapplications both for situations where the institution, through actionsby the relationship manager, (1) proactively engages the loan arrangingentities (e.g., the dealerships) to negotiate the terms and (2)reactively engages the loan arranging entities to negotiate the terms.In this manner, processor 102 may utilize predictive model 114 todetermine whether proactively engaging the loan arranging entities canpositively affect the institution's likelihood of winning the bid tofund the loan applications.

In some embodiments, processor 102 may present the prediction results tousers, e.g., relationship managers working for the institutions, to helpthe relationship managers decide whether to proactively engage any ofthe loan applications pending actions in their respective workingqueues. In some embodiments, processor 102 may rank the predictionresults to form smart working queues 116 for the relationship managers.Processor 102 may report smart working queues 116 to the relationshipmanagers through user interface 108. Smart working queues 116 maypresent the loan applications to the relationship managers according toa ranking order determined based on the prediction results.

In some embodiments, processor 102 may build predictive model 114 as agradient boosting machine (GBM) model. Gradient boosting is amachine-learning technique for regression and classification. In someembodiments, the gradient boosting machine may be configured to producepredictive model 114 as an ensemble of predictive models, e.g., decisiontrees, which can be utilized to predict the probability of aninstitution funding a loan application both with and without proactiveengagement of a relationship manager working for the institution.

In some embodiments, processor 102 may specify a dependent variable ofpredictive model 114 to represent the outcome of the loan applications.For example, in some embodiments, the outcome may indicate whether aloan application has reached the status of “funded” or “not funded”within a specified time period (e.g., 30 days) from the applicationsubmission date. In some embodiments, unfunded loan applications mayexpire after the time period has lapsed after their respectivesubmission dates.

In some embodiments, processor 102 may build predictive model 114 usingtraining data containing historical loan applications. The historicalloan applications may include applications that have either been fundedby the institution or have expired without being funded by theinstitution. In some embodiments, processor 102 may retrieve thehistorical loan applications from one or more databases 112 maintainedby the institution. In some embodiments, database 112 may recordhistorical loan applications submitted to the institution along withtheir corresponding status updates. Records stored in database 112 maybe updated, for example, every time a relationship manager at theinstitution or a sales associate at a dealership works on acorresponding loan application recorded in database 112.

In some embodiments, processor 102 may also specify different categoriesof descriptive attributes of historical loan applications for predictivemodel 114 to identify characteristics that are important to the outcomesof the historical loan applications (e.g., funded or not funded). One ofthe categories may include, for example, applicant's credit information,such as a FICO® score, a credit rating, or the like. Other categoriesmay include, for example, deal structure (e.g., loan amount, downpayment, annual percentage rate, etc.), line of credit information(including, e.g., whether the applicant has a business line of creditthrough the institution), approval status (including, e.g., whether theloan is approved with any conditions or the like), dealer information(including, e.g., service levels, dealer credit, etc.), and dynamicfeatures that may change over time (including, e.g., age of the loanapplication, level of dealer engagement, etc.). It is contemplated thatadditional or alternative features that can capture the dealerrelationship, deal structure, and credit information, may also bespecified without departing from the spirit and scope of the presentdisclosure.

In some embodiments, processor 102 may further specify a category ofdescriptive attribute referred to as “engagement information.”Engagement information for a loan application may indicate, for example,whether or not a relationship manger engaged the dealership (or theloan-arranging entity in general) to work on the loan application. Insome embodiments, the engagement information may be recorded in database112 and processor 102 may retrieve the engagement information fromdatabase 112 to form a part of the training data.

In some embodiments, the engagement information for a loan applicationmay further include data regarding whether a relationship mangerproactively or reactively engaged the dealership (or the loan arrangingentity in general) to work on the loan application. Processor 102 may,for example, request the relationship managers to indicate whether theyproactively or reactively engaged the loan arranging entities to work onloan applications. Processor 102 may record the engagement informationreceived from the relationship managers in database 112 accordingly.

It is contemplated that knowing whether a relationship mangerproactively or reactively engaged a dealership to work on a loanapplication may further improve the accuracy of predictive model 114.Generally, if a dealership contacts a relationship manager to solicitassistance during the underwriting and funding process (in other words,the relationship manager reactively engages the dealership to work onthe loan application), the relationship manager may have a greaterchance of winning the bid for the institution to fund the loanapplication. On the other hand, more effort may be required of therelationship manager if the relationship manager proactively engages thedealership to work on the loan application. This is because thedealership may have already started working with another institution tofund the loan, in which case the dealership may only consider therelationship manager's bid if the bid is more attractive compared tothat offered by the other institution. It is also possible that the loanhas already been funded by the other institution, in which caseproactively engaging the dealership to work on the loan application willnot be worthwhile for the relationship manager. Therefore, knowingwhether the relationship manger proactively or reactively engageddealerships to work on loan applications may improve the accuracy ofpredictive model 114, and in some embodiments, processor 102 may buildpredictive model 114 using training data that contains engagementinformation for the historical loan applications.

In some embodiments, processor 102 may organize the training data as aset of “snapshots.” For example, in some embodiments, processor 102 mayorganize the training data as a set of ten snapshots, and each snapshotmay include historical loan applications previously pending in a workingqueue of a relationship manager. Furthermore, in some embodiments, eachsnapshot may specify a time window, and only historical loanapplications pending within the time window may be included in thatsnapshot. For example, the time window may be specified as a seven-daywindow. In this manner, the training data may include a snapshotcontaining historical loan applications pending in the working queue ofthe relationship manager within the last seven days. The snapshot mayalso contain information regarding the various categories of descriptiveattributes associated with the historical loan applications. Suchdescriptive attributes may include, e.g., applicant's creditinformation, deal structure, line of credit information, approvalstatus, dealer information, as well as dynamic features, as describedabove.

In some embodiments, processor 102 may create snapshots according to apredetermined interval. For example, in some embodiments, processor 102may create a snapshot every three days (with each snapshot covering aseven-day window) so that there is an overlap of loan applicationsbetween two adjacent snapshots. This overlap may provide continuity thatallows predictive model 114 to analyze loan applications as theyprogress through the approval process, which may in turn help improvethe accuracy of predictive model 114. In some embodiments, processor 102may discard older snapshots and keep only a predetermined number ofsnapshots in the training data. In some embodiment, processor 102 maykeep ten snapshots in the training data. It is to be understood,however, that processor 102 may keep a different number of snapshotswithout departing from the spirit and scope of the present disclosure.

Additionally, in some embodiments, processor 102 may select to includeonly matured loan applications in the training data. Matured loanapplications may include applications that have either been funded bythe relationship manager's institution, or have expired without beingfunded by the institution. In some embodiments, processor 102 mayexclude loan applications that have been cancelled.

In some embodiments, processor 102 may filter the training data to onlyinclude loan applications that are consistent with the relationshipmanager's lines of business. For example, if the relationship manageronly works with certain types of loan applications (e.g., truck loanapplications), processor 102 may select to include only those types ofloan applications in the training data. It is contemplated thatfiltering training data in this manner may help tailor predictive model114 for a particular relationship manager, which may in turn helpimprove the accuracy of predictive model 114 for that particularrelationship manager.

Once processor 102 finishes building predictive model 114, processor 102may utilize predictive model 114 to predict a first probability of theinstitution funding a new loan application if the institution, throughactions by the relationship manager, proactively engages the loanarranging entity of the loan application (the dealership in the exampleabove). Processor 102 may also utilize predictive model 114 to predict asecond probability of the institution funding the loan application ifthe institution, through actions by the relationship manager, reactivelyengages the loan arranging entity. In some embodiments, processor 102may report the difference between the first and second probabilitiesthrough user interface 108 to help the relationship manager decidewhether it is worthwhile for the relationship manager to proactivelyengage the loan arranging entity. The difference between the first andsecond probabilities may be referred to as a model score. Generally, agreater model score suggests that proactively engaging the dealershipwill produce a greater impact on the probability of the institutionfunding the loan application. It is to be understood that processor 102may continue to train predictive model 114 on an ongoing basis as newrecords are being stored into database 112.

In some embodiments, processor 102 may utilize predictive model 114 topredict the first and second probabilities for each loan applicationpending actions by the relationship manager. Processor 102 may thencalculate and report a model score for each loan application to therelationship manager through user interface 108. The relationshipmanager may use user interface 108 to rank the loan applications basedon their corresponding model scores. For example, the relationshipmanager may choose to rank the loan applications based on the modelscores in a descending order. In this manner, the relationship managermay determine which loan applications can benefit the most from therelationship manager's proactive engagement based on the predicted modelscores.

Processor 102 may also apply additional mathematical operations to themodel scores to calculate a final score for each loan application. Forexample, in some embodiments, processor 102 may multiply the modelscores by appropriate scalars to calculate the financial values of theloan applications. In some embodiments, processor 102 may determineappropriate scalars based on the type of the loan applications. Forexample, personal loans and commercial loans may be assigned differentscalars by the institution. Alternatively or additionally, loans fordifferent dollar amount may be assigned different scalars.

In some embodiments, processor 102 may also take into account theexpected profitability of the loans and scale the financial values ofthe loan applications accordingly. This “profitability scalar” may beused to increase the financial value of a loan that would otherwisegarner a lower financial value or vice versa. For example, in someembodiments, processor 102 may score a highly profitable loan with ahigher financial value compared to a loan that has a lowerprofitability, even if predictive model 114 suggests that the highlyprofitable loan is less likely to be funded compared to the loan thathas the lower profitability.

Processor 102 may further apply time factor adjustments to calculate thefinal scores. For example, in some embodiments, processor 102 may dividethe financial value calculated for each loan application by an averageworking time required to work on loan applications of the same type. Inthis manner, processor 102 may be able to calculate a final score foreach loan application that can represent the financial value of the loanapplication per unit of time. The relationship manager may further useuser interface 108 to rank the loan applications based on theircorresponding financial values per unit of time. The relationshipmanager may, for example, choose to rank the loan applications based ontheir financial values per unit of time in a descending order. Therelationship manager may determine which loan applications can benefitthe most from the relationship manager's proactive engagement per unitof time.

In some embodiments, processor 102 may further adjust the financialvalues of the loan applications based on certain characteristics of theloan applications. For example, if the relationship manager has a numberof loan applications from the same dealership, processor 102 mayrecognize these loan applications as a group and increase the financialvalues of this group of loan applications. For example, the relationshipmanager may increase the financial values for a group of loanapplications because the relationship manager may be able to handle thisgroup of loan applications more efficiently (e.g., with a single phonecall to the dealership rather than multiple phone calls to multipledealerships). It is contemplated that processor 102 may increase ordecrease the financial values of the loan applications based on othercharacteristics of the loan applications without departing from thespirit and scope of the present disclosure.

It is to be understood that while predictive model 114 described abovereferenced a gradient boosting machine model, such a reference is merelypresented as an example and is not meant to be limiting. It iscontemplated that processor 102 may build predictive model 114 usingother machine-learning techniques without departing from the spirit andscope of the present disclosure. For example, processor 102 may buildpredictive model 114 using logistic regression, naive Bayes, neuralnetworks (e.g., multilayer perceptron, back-propagation, and other deeparchitectures), support vector machines, random forests, as well asother boosting algorithms or binary classification algorithms that canbe adapted to output probabilities.

It is also to be understood that processor 102 may be configured tobuild more than one predictive model 114 depending on the needs of theinstitution utilizing intelligent interface queuing system 100. Forexample, a small institution having a few relationship managers workingfor it may configure processor 102 to build just one predictive model114 and utilize the predictive model 114 to process all loanapplications pending actions by the institution collectively. On theother hand, if the institution has different lending units setup toprocess different types of loans, the institution may configureprocessor 102 to build one or more predictive models 114 for eachlending unit. Alternatively or additionally, the institution mayconfigure processor 102 to build a predictive model 114 for eachrelationship manager. Likewise, in some embodiments, the institution mayconfigure processor 102 to build different predictive models 114 fordifferent dealerships that it conducts business with, or for differentgeographic regions or other types of clusters (e.g., clusters based oncredit profiles such as prime, near prime, subprime, etc.) withoutdeparting from the spirit and scope of the present disclosure.

Referring now to FIG. 3, there is shown a flow diagram illustrating anexemplary method 300 for optimizing processing of loan applications.While method 300 is described herein as a sequence of steps, it is to beunderstood that the order of the steps may vary in otherimplementations. In particular, steps may be performed in any order, orin parallel. It is to be understood that steps of method 300 may beperformed by one or more processors, computers, servers, controllers orthe like.

In some embodiments, method 300 may be performed by intelligentinterface queuing system 100 (as depicted in FIG. 1). At step 302,method 300 may analyze training data to build a predictive model. Insome embodiments, method 300 may build the predictive model utilizing amachine-learning system trained using the training data. Themachine-learning system may include a gradient boosting machine. In someembodiments, the training data may include snapshots of historical dataobtained at different times. Each snapshot may include historical loanapplications previously pending in a working queue of a user (e.g., arelationship manager working for an institution utilizing method 300).Each snapshot may be created according to rules described above. In someembodiments, applications included in the snapshots must have beenfunded or expired.

At step 304, method 300 may apply the predictive model to new loanapplications to determine the probabilities that the institution willfund the new loan applications. Specifically, method 300 may determine afirst probability of the institution funding a new loan application ifthe institution, through actions by its relationship manager,proactively engages a loan arranging entity of that loan application.Method 300 may also determine a second probability of the institutionfunding that loan application if the institution, through actions by itsrelationship manger, reactively engages the loan arranging entity ofthat loan application.

In some embodiments, the institution may receive the loan applicationsfrom loan arranging entities (e.g., dealerships). In some embodiments,the loan arranging entities may send the loan applications to one ormore intermediaries, which may then distribute the loan applications tomultiple financial institutions. The institution may record theinformation received and present the information via an interface to therelationship manager. FIG. 4 is an illustration depicting an exemplaryinterface 400 that may be utilized to present the loan applications to arelationship manager. The relationship manager may utilize interface 400to work on the loans back and forth with the loan arranging entities(e.g., dealerships). For example, the relationship manager may provide aresponse back to the dealership with proposed loan terms for a loanapplication 402 and wait for further communications from the dealership.The loan application 402 may remain pending in the queue until it isfunded, cancelled, or otherwise acted upon by either the relationshipmanager or the dealership.

Returning to FIG. 3, at step 306, method 300 may determine a value ofeach new loan application. In some embodiments, the value of each newloan application may be determined based on the difference between thefirst and second probabilities determined for that loan application. Insome embodiments, the value of each new loan application may bemultiplied by a scalar provided for that loan application. In someembodiments, the value of each new loan application may be furtherdivided by an average working time required for that loan application.Furthermore, in some embodiments, the values of some loan applicationsmay be adjusted based on certain characteristics of the loanapplications, including, e.g., the expected profitability of the loanapplications and the like.

In some embodiments, step 306 may be carried out upon receiving a newapplication. In this manner, method 300 may determine the value of thenew loan application to assist the relationship manager in decidingwhether to proactively engage the loan application. In some embodiments,step 306 may also be carried out when an existing application receivesupdated information. For example, if a loan application receives aco-signer, the value of the loan application may be re-evaluated.Likewise, if the dealership responds to terms proposed by therelationship manager for a loan application, the value of that loanapplication may be re-evaluated based on the response receive. It iscontemplated that step 306 may be carried out in other instances toevaluate or re-evaluate loan applications without departing from thespirit and scope of the present disclosure.

In some embodiments, step 306 may be carried out to re-evaluate loanapplications throughout their lifecycle. In some embodiments, the age ofthe loan applications may be a dynamic feature taken into considerationby step 306, which may update the scores calculated for the loanapplications as the loan applications progress through their lifecycle.In some embodiments, users (e.g., relationship managers) may alsoutilize a refresh button provided on the user interface 400 to initiatethe re-evaluation process.

At step 308, method 300 may rank the new loan applications based ontheir first and second probabilities and/or their determined values. Atstep 310, method 300 may report the new loan application in a rankingorder to the user. The ranking order may suggest to the user (e.g., therelationship manager working for the institution) which loanapplications can benefit the most from the user's proactive engagement.In some embodiments, the user may customize the report and modify theranking based on their preferences. The user may, for example, customizethe ranking by applying one or more data filters 404 made availablethrough the user interface 400. The user may request display of onlyloan applications sent from a certain dealerships or intermediaries. Insome embodiments, the user may have the option to turn off the rankingusing a switch 406 provided on the user interface 400. The user may thenchoose to sort the loan applications using various types of datacategories 408 without departing from the spirit and scope of thepresent disclosure.

In some examples, some or all of the logic for the above-describedtechniques may be implemented as a computer program or application or asa plug-in module or subcomponent of another application. The describedtechniques may be varied and are not limited to the examples ordescriptions provided.

Moreover, while illustrative embodiments have been described herein, thescope thereof includes any and all embodiments having equivalentelements, modifications, omissions, combinations (e.g., of aspectsacross various embodiments), adaptations and/or alterations as would beappreciated by those in the art based on the present disclosure. Forexample, the number and orientation of components shown in the exemplarysystems may be modified. Further, with respect to the exemplary methodsillustrated in the attached drawings, the order and sequence of stepsmay be modified, and steps may be added or deleted.

Thus, the foregoing description has been presented for purposes ofillustration only. It is not exhaustive and is not limiting to theprecise forms or embodiments disclosed. Modifications and adaptationswill be apparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments.

The claims are to be interpreted broadly based on the language employedin the claims and not limited to examples described in the presentspecification, which examples are to be construed as non-exclusive.Further, the steps of the disclosed methods may be modified in anymanner, including by reordering steps and/or inserting or deletingsteps.

Furthermore, although aspects of the disclosed embodiments are describedas being associated with data stored in memory and other tangiblecomputer-readable storage mediums, one skilled in the art willappreciate that these aspects may also be stored on and executed frommany types of tangible computer-readable media, such as secondarystorage devices, like hard disks, floppy disks, or CD-ROM, or otherforms of RAM or ROM. Accordingly, the disclosed embodiments are notlimited to the above described examples, but instead is defined by theappended claims in light of their full scope of equivalents.

What is claimed is:
 1. A system, comprising: one or more databasesincluding training data stored therein; one or more memory devicesstoring instructions; one or more processors configured to execute theinstructions; and one or more interfaces for managing interactionsbetween the one or more processors and the one or more databases, theone or more processors configured to execute the instructions to performoperations comprising: analyzing the training data stored in the one ormore databases, and building a predictive model based on the analyzedtraining data; applying the predictive model to a plurality of loanapplications, and determining for each loan application of the pluralityof loan applications: a first probability of an institution funding theloan application if the institution proactively engages a loan-arrangingentity of the loan application; and a second probability of theinstitution funding the loan application if the institution reactivelyengages the loan-arranging entity; reporting the first and secondprobabilities to a user through a user interface of the one or moreinterfaces; updating the predictive model; determining a value of eachloan application of the plurality of loan applications by: determiningdifferences between the first and second probabilities for eachrespective loan application; and multiplying the differences by scalarsprovided for the loan applications, and determining scaled values;ranking the plurality of loan applications based on the first and secondprobabilities; and reporting the plurality of loan applications in aranking order to the user through the user interface, wherein theanalyzing the training data includes building the predictive modelutilizing a machine-learning system trained using the training data, andwherein the machine-learning system comprises a gradient boostingmachine.
 2. The system of claim 1, wherein the training data compriseshistorical loan applications and engagement information about thehistorical loan applications.
 3. The system of claim 1, wherein thetraining data includes a plurality of snapshots of historical dataobtained at different times.
 4. The system of claim 3, wherein eachsnapshot comprises a plurality of loan applications previously pendingin a working queue of the user.
 5. The system of claim 4, wherein eachof the previously pending applications has a status of one of funded orexpired.
 6. The system of claim 1, wherein the operations furthercomprise: reporting the respective values to the user through the userinterface.
 7. The system of claim 1, wherein the loan-arranging entityis an automobile dealership.
 8. The system of claim 1, wherein thedetermining the values further comprises: dividing the scaled values byaverage working times required of the loan applications, and determiningvalues of the loan applications per unit of time.
 9. A method,comprising: analyzing training data stored in a database, and building apredictive model based on the analyzed training data; applying thepredictive model to a plurality of loan applications, and determiningfor each loan application of the plurality of loan applications: a firstprobability of an institution funding the loan applications if theinstitution proactively engages loan-arranging entities of the loanapplications; and a second probability of the institution funding theloan applications if the institution reactively engages theloan-arranging entities of the loan applications; updating thepredictive model; ranking the loan applications based on the first andsecond probabilities; reporting the loan applications in a ranking orderto a user through a user interface; and determining a value of each loanapplication of the plurality of loan applications by: determiningdifferences between the first and second probabilities for eachrespective loan application; and multiplying the differences by scalarsprovided for the loan applications, and determining scaled values,wherein the analyzing the training data includes building the predictivemodel utilizing a machine-learning system trained using the trainingdata, and wherein the machine-learning system comprises a gradientboosting machine.
 10. The method of claim 9, further comprising:reporting the respective values to the user through the user interface.11. The method of claim 9, wherein the training data compriseshistorical loan applications and engagement information about thehistorical loan applications.
 12. The method of claim 9, wherein thetraining data includes a plurality of snapshots of historical dataobtained at different times.
 13. The method of claim 12, wherein: eachsnapshot comprises a plurality of loan applications previously pendingin a working queue of the user; and each of the previously pendingapplications has a status of one of funded or expired.
 14. The method ofclaim 9, wherein the determining the values further comprises: dividingthe scaled values by average working times required of the loanapplications, and determining values of the loan applications per unitof time.
 15. The method of claim 9, further comprising: reporting thefirst and second probabilities to the user through the user interface.16. The method of claim 9, wherein the loan-arranging entity is anautomobile dealership.
 17. A non-transitory memory storing instructionsthat, when executed by at least one processor, cause a system to performoperations comprising: analyzing training data stored in a database, andbuilding a predictive model based on the analyzed training data;applying the predictive model to a plurality of loan applications, anddetermining for each loan application of the plurality of loanapplications: a first probability of an institution funding the loanapplications if the institution proactively engages loan-arrangingentities of the loan applications; and a second probability of theinstitution funding the loan applications if the institution reactivelyengages the loan-arranging entities of the loan applications; updatingthe predictive model; ranking the loan applications based on the firstand second probabilities; reporting the loan applications in a rankingorder to a user through a user interface; determining a value of eachloan application of the plurality of loan applications by: determiningdifferences between the first and second probabilities for eachrespective loan application; and multiplying the differences by scalarsprovided for the loan applications, and determining scaled values; andreporting the respective values to the user through the user interface;wherein the analyzing the training data includes building the predictivemodel utilizing a machine-learning system trained using the trainingdata, and wherein the machine-learning system comprises a gradientboosting machine.
 18. The non-transitory memory of claim 17, wherein thetraining data comprises historical loan applications and engagementinformation about the historical loan applications, and the trainingdata including a plurality of snapshots of historical data obtained atdifferent times.
 19. The non-transitory memory of claim 17, wherein theloan-arranging entity is an automobile dealership.
 20. Thenon-transitory memory of claim 17, wherein the determining the valuesfurther comprises: dividing the scaled values by average working timesrequired of the loan applications, and determining values of the loanapplications per unit of time.